Design and analysis of metastable-hardened and soft-error tolerant high-performance, low-power flip-flops
Bibliographic record
Abstract
In this paper, detailed analysis is given on the design of metastable-hardened and soft-error tolerant flip-flops while maintaining the basic characteristics of low-power and high-performance. We also propose two new flip-flop designs: pre-discharge soft-error tolerant flip-flop (PDFF-SE) and sense-amplifier transmission-gate soft-error tolerant flip-flop (SATG-SE). Following our main design approach, both PDFF-SE and SATG-SE use a cross-coupled inverter on the critical path in the master-stage to achieve good metastability while generating differential signals to facilitate the usage of the Quatro cell in the slave-stage to protect against soft-errors. PDFF-SE is designed to achieve very high performance with good metastability while SATG-SE is a low-power design also with good metastability. We also introduce two new design metrics, namely the metastability-delay-product (MDP) and the metastability-power-delay-product (MPDP), to analyze the design tradeoffs between metastability, power, and performance. Simulation results in 65 nm CMOS technology have shown that both proposed designs achieve significant reduction in MDP and MPDP when compared to other flip-flop architectures analyzed in this work. Monte Carlo simulation results also show that these flip-flops are very robust and reliable against process variations and mismatches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".